The present embodiments relate to processing medical diagnostic images.
Accurate and timely segmentation and modeling of vessels or lumens is a challenging task in many applications. For example, intensity contrast may change drastically along vessels; vessels may touch each other near bright structures such as bone or other vessels; a single vessel tree can have large and small vessels due to scale change; and local vessel structure may deviate from a tubular shape due to the presence of pathological conditions such as stenosis.
For vessels or lumens, existing models use centerlines of branches or boundaries detected by an automated algorithm or manually annotated by a medical imaging operator. The models may be learning based or hand-crafted models built with only local image features.
Segmenting the boundary of a vessel or lumen with high precision is a challenging task as even the main coronary arteries have diameters of only a few millimeters. In addition, the contrast of the vessel or lumen, that may be visually enhanced by contrast dye, is in between the contrast of non-calcified and calcified plaque that complicates an accurate distinction. The segmentation of a lumen obtained by existing segmentation algorithms may be misled by calcifications leading to over-estimation or under-estimation of the boundaries. In addition, the correct visualization of the coronary anatomy is made difficult by the anatomical complexity of the coronary tree that may result in projections that are difficult to read and identify. Since the diagnostic value of non-invasive procedures relies on accurate modeling of the vessel or lumen, a precise segmentation is desired.